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Cracking the Data Engineering Interview

You're reading from   Cracking the Data Engineering Interview Land your dream job with the help of resume-building tips, over 100 mock questions, and a unique portfolio

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837630776
Length 196 pages
Edition 1st Edition
Languages
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Authors (2):
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Kedeisha Bryan Kedeisha Bryan
Author Profile Icon Kedeisha Bryan
Kedeisha Bryan
Taamir Ransome Taamir Ransome
Author Profile Icon Taamir Ransome
Taamir Ransome
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Landing Your First Data Engineering Job
2. Chapter 1: The Roles and Responsibilities of a Data Engineer FREE CHAPTER 3. Chapter 2: Must-Have Data Engineering Portfolio Projects 4. Chapter 3: Building Your Data Engineering Brand on LinkedIn 5. Chapter 4: Preparing for Behavioral Interviews 6. Part 2: Essentials for Data Engineers Part I
7. Chapter 5: Essential Python for Data Engineers 8. Chapter 6: Unit Testing 9. Chapter 7: Database Fundamentals 10. Chapter 8: Essential SQL for Data Engineers 11. Part 3: Essentials for Data Engineers Part II
12. Chapter 9: Database Design and Optimization 13. Chapter 10: Data Processing and ETL 14. Chapter 11: Data Pipeline Design for Data Engineers 15. Chapter 12: Data Warehouses and Data Lakes 16. Part 4: Essentials for Data Engineers Part III
17. Chapter 13: Essential Tools You Should Know 18. Chapter 14: Continuous Integration/Continuous Development (CI/CD) for Data Engineers 19. Chapter 15: Data Security and Privacy 20. Chapter 16: Additional Interview Questions
21. Index 22. Other Books You May Enjoy

Understanding database design essentials

In this section, we will delve into the fundamental principles of database design that are essential for every data engineer. Database design is the process of creating a detailed model of a database. This defines how the data is stored, organized, and manipulated. A well-designed database will be dependent on how the data engineer makes decisions regarding correct data types, constraints, schema design, and entity-relational (ER) modeling. This will ensure the data integrity, performance, and reliability of the database.

We will begin by discussing database normalization and the different types:

  • Data normalization: A procedure that eliminates data duplication and guarantees data integrity. Normalization normally occurs in application databases as opposed to data warehouses. We use normal forms to guide the normalization process. The most common forms include the following:
    • First Normal Form (1NF): Ensures atomicity by organizing data...
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